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# Automated Interview Filtering
## Overview
The HR Interview Analysis System is an AI-powered tool that helps HR professionals streamline their interview process through automated analysis of video interviews, resumes, and candidate responses. The system leverages multiple AI technologies including emotion detection, speech-to-text conversion, and natural language processing to provide comprehensive candidate assessments.
## Architecture
The project follows a clean, **layered monolith architecture**:
```
src/
├── domain/ # Core business logic and entities
├── service/ # Use cases and business rules
├── frontend/ # UI and API interfaces
└── utils/ # Helper functions and utilities
tests/
└── integration/ # Integration tests
.gitignore # Git ignore file
README.md # Project documentation
requirements.txt # Dependencies
```
### Key Components
- **Domain Layer**: Contains business entities, value objects, and enums
- **Service Layer**: Core business logic and use cases
- LangChain for LLM integration
- DeepFace for emotion analysis
- Google Speech-to-Text for audio transcription
- LlamaParse for resume parsing
- **Frontend Layer**: Gradio-based user interface
- **Utils**: Helper functions and utilities
## Technologies Used
- **Frontend**: Gradio
- **AI/ML**:
- LangChain for LLM operations
- DeepFace for facial emotion analysis
- Google Speech-to-Text API
- LlamaParse for resume parsing
- **Development Tools**:
- Python 3.9+
- Black for code formatting
- pytest for testing
- pre-commit hooks
## Prerequisites
- Python 3.9 or higher
- pip package manager
- Git
## Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/hr-interview-analyzer.git
cd hr-interview-analyzer
```
2. Create and activate virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Set up environment variables:
```bash
cp .env.example .env
# Edit .env with your API keys and configurations
```
## Development Setup
### Code Formatting
We use Black for code formatting. To set up:
1. Install pre-commit hooks:
```bash
pre-commit install
```
2. Run Black manually:
```bash
black .
```
3. Configure VS Code (optional):
```json
{
"python.formatting.provider": "black",
"editor.formatOnSave": true
}
```
### Git Workflow
#### Creating a New Branch
```bash
# Update main branch
git checkout main
git pull origin main
# Create new feature branch
git checkout -b feature/your-feature-name
```
#### Making Changes
```bash
# Stage changes
git add .
# Commit changes
git commit -m "feat: your descriptive commit message"
# Push to remote
git push origin feature/your-feature-name
```
### Running the Application
#### Starting Gradio Interface
```bash
python -m src.presentation.gradio.interface
```
The interface will be available at `http://localhost:7860`
#### Running Tests
```bash
# Run all tests
pytest
# Run specific test file
pytest tests/unit/test_interview_analyzer.py
# Run with coverage
pytest --cov=src tests/
```
## Configuration
### Environment Variables
```env
OPENAI_API_KEY=your_key_here
GOOGLE_SPEECH_KEY=your_key_here
LLAMAPARSE_API_KEY=your_key_here
```
### Supported File Formats
- Video: MP4, AVI, MOV, WMV
- Resume: PDF, DOCX, DOC, TXT
## Error Handling
The system implements comprehensive error handling for:
- Invalid file formats
- API failures
- Resource limitations
- Processing errors
## Contributing
1. Clone the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request
### Commit Message Format
```
[<type>]: <subject>
[<body>]
[<footer>]
```
Types:
- feat: New feature
- fix: Bug fix
- docs: Documentation
- style: Formatting
- refactor: Code restructuring
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